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Related Concept Videos

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Related Experiment Video

Updated: Jun 24, 2026

A Method to Study Adaptation to Left-Right Reversed Audition
07:14

A Method to Study Adaptation to Left-Right Reversed Audition

Published on: October 29, 2018

Individual differences within and across feedback suppression hearing aids.

Todd Ricketts1, Earl Johnson, Jeremy Federman

  • 1Department of Hearing and Speech Sciences, Vanderbilt Bill Wilkerson Center, Nashville, TN 37232-8242, USA. todd.a.ricketts@vanderbilt.edu

Journal of the American Academy of Audiology
|April 11, 2009
PubMed
Summary
This summary is machine-generated.

Comparisons of hearing aid feedback suppression may be misleading. Acoustic manikin measurements can differ significantly from real-world listener results, highlighting the need for careful clinical selection of feedback suppression algorithms.

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Last Updated: Jun 24, 2026

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Systematic Hearing Performance Evaluation Process for Adolescents with Cochlear Implantation at Early Ages
06:04

Systematic Hearing Performance Evaluation Process for Adolescents with Cochlear Implantation at Early Ages

Published on: March 24, 2023

Area of Science:

  • Audiology
  • Hearing aid technology

Background:

  • Hearing aids utilize feedback suppression (FS) to prevent acoustic feedback.
  • Comparing additional gain before feedback (AGBF) across devices is challenging due to listener and measurement variability.

Purpose of the Study:

  • To investigate the variability of AGBF values in individual listeners and an acoustic manikin.
  • To assess the reliability of AGBF measurements across different FS algorithms.

Main Methods:

  • A descriptive study evaluated AGBF in six commercial FS algorithms.
  • Probe microphone techniques were used with 16 participants and an acoustic manikin.

Main Results:

  • AGBF ranged from 0 to 15 dB across FS algorithms.
  • Acoustic manikin measurements may be misleading for small hearing aid differences (<6 dB).
  • Individual listener variability in AGBF ranged from 7 to 16 dB within the same algorithm.

Conclusions:

  • Clinical selection of FS algorithms requires caution.
  • Averaged AGBF may mask significant individual differences, impacting real-world performance.